Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations6014
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory657.8 KiB
Average record size in memory112.0 B

Variable types

Text1
Numeric8
Categorical4

Alerts

engine is highly overall correlated with max_power and 2 other fieldsHigh correlation
km_driven is highly overall correlated with yearHigh correlation
max_power is highly overall correlated with engine and 3 other fieldsHigh correlation
seats is highly overall correlated with engineHigh correlation
selling_price is highly overall correlated with max_power and 2 other fieldsHigh correlation
torque is highly overall correlated with engine and 2 other fieldsHigh correlation
transmission is highly overall correlated with max_powerHigh correlation
year is highly overall correlated with km_driven and 1 other fieldsHigh correlation
seller_type is highly imbalanced (68.2%) Imbalance
transmission is highly imbalanced (58.2%) Imbalance

Reproduction

Analysis started2024-11-27 18:38:03.335862
Analysis finished2024-11-27 18:38:44.452259
Duration41.12 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

name
Text

Distinct1924
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Memory size94.0 KiB
2024-11-27T18:38:45.934019image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length54
Median length43
Mean length25.166279
Min length11

Characters and Unicode

Total characters151350
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique905 ?
Unique (%)15.0%

Sample

1st rowMaruti Swift Dzire VDI
2nd rowSkoda Rapid 1.5 TDI Ambition
3rd rowHyundai i20 Sportz Diesel
4th rowMaruti Swift VXI BSIII
5th rowHyundai Xcent 1.2 VTVT E Plus
ValueCountFrequency (%)
maruti 1886
 
6.6%
hyundai 1083
 
3.8%
mahindra 629
 
2.2%
swift 592
 
2.1%
tata 544
 
1.9%
bsiv 507
 
1.8%
diesel 470
 
1.7%
1.2 435
 
1.5%
vxi 425
 
1.5%
vdi 420
 
1.5%
Other values (828) 21405
75.4%
2024-11-27T18:38:49.602796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22383
 
14.8%
a 11047
 
7.3%
i 10130
 
6.7%
t 7669
 
5.1%
r 6677
 
4.4%
o 5992
 
4.0%
n 5773
 
3.8%
e 5550
 
3.7%
u 4474
 
3.0%
S 4090
 
2.7%
Other values (58) 67565
44.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 151350
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
22383
 
14.8%
a 11047
 
7.3%
i 10130
 
6.7%
t 7669
 
5.1%
r 6677
 
4.4%
o 5992
 
4.0%
n 5773
 
3.8%
e 5550
 
3.7%
u 4474
 
3.0%
S 4090
 
2.7%
Other values (58) 67565
44.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 151350
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
22383
 
14.8%
a 11047
 
7.3%
i 10130
 
6.7%
t 7669
 
5.1%
r 6677
 
4.4%
o 5992
 
4.0%
n 5773
 
3.8%
e 5550
 
3.7%
u 4474
 
3.0%
S 4090
 
2.7%
Other values (58) 67565
44.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 151350
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
22383
 
14.8%
a 11047
 
7.3%
i 10130
 
6.7%
t 7669
 
5.1%
r 6677
 
4.4%
o 5992
 
4.0%
n 5773
 
3.8%
e 5550
 
3.7%
u 4474
 
3.0%
S 4090
 
2.7%
Other values (58) 67565
44.6%

year
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.4475
Minimum1983
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size94.0 KiB
2024-11-27T18:38:50.276802image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1983
5-th percentile2006
Q12011
median2014
Q32017
95-th percentile2019
Maximum2020
Range37
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0799204
Coefficient of variation (CV)0.0020263357
Kurtosis1.6891091
Mean2013.4475
Median Absolute Deviation (MAD)3
Skewness-1.0198946
Sum12108873
Variance16.64575
MonotonicityNot monotonic
2024-11-27T18:38:50.920494image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2017 696
11.6%
2016 615
10.2%
2015 585
9.7%
2018 536
8.9%
2012 524
8.7%
2014 503
8.4%
2013 488
8.1%
2011 469
7.8%
2010 329
 
5.5%
2019 306
 
5.1%
Other values (19) 963
16.0%
ValueCountFrequency (%)
1983 1
 
< 0.1%
1991 1
 
< 0.1%
1994 3
 
< 0.1%
1995 1
 
< 0.1%
1996 3
 
< 0.1%
1997 10
0.2%
1998 9
0.1%
1999 12
0.2%
2000 19
0.3%
2001 7
 
0.1%
ValueCountFrequency (%)
2020 59
 
1.0%
2019 306
5.1%
2018 536
8.9%
2017 696
11.6%
2016 615
10.2%
2015 585
9.7%
2014 503
8.4%
2013 488
8.1%
2012 524
8.7%
2011 469
7.8%

selling_price
Real number (ℝ)

High correlation 

Distinct637
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521982.03
Minimum29999
Maximum10000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size94.0 KiB
2024-11-27T18:38:51.659909image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum29999
5-th percentile100000
Q1250000
median409999
Q3640000
95-th percentile1200000
Maximum10000000
Range9970001
Interquartile range (IQR)390000

Descriptive statistics

Standard deviation533842.62
Coefficient of variation (CV)1.0227222
Kurtosis52.713853
Mean521982.03
Median Absolute Deviation (MAD)190001
Skewness5.6236928
Sum3.1391999 × 109
Variance2.8498794 × 1011
MonotonicityNot monotonic
2024-11-27T18:38:52.552999image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300000 188
 
3.1%
350000 173
 
2.9%
400000 147
 
2.4%
250000 144
 
2.4%
550000 142
 
2.4%
600000 142
 
2.4%
500000 138
 
2.3%
450000 137
 
2.3%
200000 133
 
2.2%
650000 125
 
2.1%
Other values (627) 4545
75.6%
ValueCountFrequency (%)
29999 1
 
< 0.1%
30000 2
 
< 0.1%
31504 1
 
< 0.1%
35000 3
 
< 0.1%
39000 1
 
< 0.1%
40000 12
0.2%
42000 2
 
< 0.1%
45000 16
0.3%
45957 1
 
< 0.1%
50000 15
0.2%
ValueCountFrequency (%)
10000000 1
 
< 0.1%
7200000 1
 
< 0.1%
6523000 1
 
< 0.1%
6223000 1
 
< 0.1%
6000000 3
< 0.1%
5923000 1
 
< 0.1%
5850000 1
 
< 0.1%
5830000 1
 
< 0.1%
5800000 2
< 0.1%
5500000 3
< 0.1%

km_driven
Real number (ℝ)

High correlation 

Distinct827
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73764.15
Minimum1
Maximum2360457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size94.0 KiB
2024-11-27T18:38:53.355713image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11325
Q139000
median70000
Q3100000
95-th percentile155000
Maximum2360457
Range2360456
Interquartile range (IQR)61000

Descriptive statistics

Standard deviation59610.747
Coefficient of variation (CV)0.80812627
Kurtosis416.83816
Mean73764.15
Median Absolute Deviation (MAD)30000
Skewness12.58954
Sum4.436176 × 108
Variance3.5534411 × 109
MonotonicityNot monotonic
2024-11-27T18:38:53.800894image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120000 446
 
7.4%
70000 373
 
6.2%
80000 371
 
6.2%
60000 342
 
5.7%
50000 313
 
5.2%
100000 290
 
4.8%
90000 280
 
4.7%
40000 238
 
4.0%
110000 229
 
3.8%
30000 192
 
3.2%
Other values (817) 2940
48.9%
ValueCountFrequency (%)
1 1
 
< 0.1%
1000 5
0.1%
1300 1
 
< 0.1%
1303 1
 
< 0.1%
1500 2
 
< 0.1%
1600 1
 
< 0.1%
1620 1
 
< 0.1%
2000 6
0.1%
2118 1
 
< 0.1%
2136 1
 
< 0.1%
ValueCountFrequency (%)
2360457 1
< 0.1%
1500000 1
< 0.1%
577414 1
< 0.1%
500000 2
< 0.1%
475000 1
< 0.1%
440000 1
< 0.1%
426000 1
< 0.1%
380000 1
< 0.1%
376412 1
< 0.1%
370000 1
< 0.1%

fuel
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size94.0 KiB
Diesel
3269 
Petrol
2660 
CNG
 
51
LPG
 
34

Length

Max length6
Median length6
Mean length5.9575989
Min length3

Characters and Unicode

Total characters35829
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiesel
2nd rowDiesel
3rd rowDiesel
4th rowPetrol
5th rowPetrol

Common Values

ValueCountFrequency (%)
Diesel 3269
54.4%
Petrol 2660
44.2%
CNG 51
 
0.8%
LPG 34
 
0.6%

Length

2024-11-27T18:38:54.130142image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T18:38:54.418730image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
diesel 3269
54.4%
petrol 2660
44.2%
cng 51
 
0.8%
lpg 34
 
0.6%

Most occurring characters

ValueCountFrequency (%)
e 9198
25.7%
l 5929
16.5%
D 3269
 
9.1%
i 3269
 
9.1%
s 3269
 
9.1%
P 2694
 
7.5%
t 2660
 
7.4%
r 2660
 
7.4%
o 2660
 
7.4%
G 85
 
0.2%
Other values (3) 136
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35829
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 9198
25.7%
l 5929
16.5%
D 3269
 
9.1%
i 3269
 
9.1%
s 3269
 
9.1%
P 2694
 
7.5%
t 2660
 
7.4%
r 2660
 
7.4%
o 2660
 
7.4%
G 85
 
0.2%
Other values (3) 136
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35829
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 9198
25.7%
l 5929
16.5%
D 3269
 
9.1%
i 3269
 
9.1%
s 3269
 
9.1%
P 2694
 
7.5%
t 2660
 
7.4%
r 2660
 
7.4%
o 2660
 
7.4%
G 85
 
0.2%
Other values (3) 136
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35829
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 9198
25.7%
l 5929
16.5%
D 3269
 
9.1%
i 3269
 
9.1%
s 3269
 
9.1%
P 2694
 
7.5%
t 2660
 
7.4%
r 2660
 
7.4%
o 2660
 
7.4%
G 85
 
0.2%
Other values (3) 136
 
0.4%

seller_type
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size94.0 KiB
Individual
5394 
Dealer
595 
Trustmark Dealer
 
25

Length

Max length16
Median length10
Mean length9.6291985
Min length6

Characters and Unicode

Total characters57910
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual 5394
89.7%
Dealer 595
 
9.9%
Trustmark Dealer 25
 
0.4%

Length

2024-11-27T18:38:54.700243image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T18:38:54.949215image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
individual 5394
89.3%
dealer 620
 
10.3%
trustmark 25
 
0.4%

Most occurring characters

ValueCountFrequency (%)
d 10788
18.6%
i 10788
18.6%
a 6039
10.4%
l 6014
10.4%
u 5419
9.4%
I 5394
9.3%
v 5394
9.3%
n 5394
9.3%
e 1240
 
2.1%
r 670
 
1.2%
Other values (7) 770
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57910
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 10788
18.6%
i 10788
18.6%
a 6039
10.4%
l 6014
10.4%
u 5419
9.4%
I 5394
9.3%
v 5394
9.3%
n 5394
9.3%
e 1240
 
2.1%
r 670
 
1.2%
Other values (7) 770
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57910
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 10788
18.6%
i 10788
18.6%
a 6039
10.4%
l 6014
10.4%
u 5419
9.4%
I 5394
9.3%
v 5394
9.3%
n 5394
9.3%
e 1240
 
2.1%
r 670
 
1.2%
Other values (7) 770
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57910
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 10788
18.6%
i 10788
18.6%
a 6039
10.4%
l 6014
10.4%
u 5419
9.4%
I 5394
9.3%
v 5394
9.3%
n 5394
9.3%
e 1240
 
2.1%
r 670
 
1.2%
Other values (7) 770
 
1.3%

transmission
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size94.0 KiB
Manual
5505 
Automatic
 
509

Length

Max length9
Median length6
Mean length6.2539075
Min length6

Characters and Unicode

Total characters37611
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowManual

Common Values

ValueCountFrequency (%)
Manual 5505
91.5%
Automatic 509
 
8.5%

Length

2024-11-27T18:38:55.233154image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T18:38:55.504681image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
manual 5505
91.5%
automatic 509
 
8.5%

Most occurring characters

ValueCountFrequency (%)
a 11519
30.6%
u 6014
16.0%
M 5505
14.6%
n 5505
14.6%
l 5505
14.6%
t 1018
 
2.7%
A 509
 
1.4%
o 509
 
1.4%
m 509
 
1.4%
i 509
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37611
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11519
30.6%
u 6014
16.0%
M 5505
14.6%
n 5505
14.6%
l 5505
14.6%
t 1018
 
2.7%
A 509
 
1.4%
o 509
 
1.4%
m 509
 
1.4%
i 509
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37611
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11519
30.6%
u 6014
16.0%
M 5505
14.6%
n 5505
14.6%
l 5505
14.6%
t 1018
 
2.7%
A 509
 
1.4%
o 509
 
1.4%
m 509
 
1.4%
i 509
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37611
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11519
30.6%
u 6014
16.0%
M 5505
14.6%
n 5505
14.6%
l 5505
14.6%
t 1018
 
2.7%
A 509
 
1.4%
o 509
 
1.4%
m 509
 
1.4%
i 509
 
1.4%

owner
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size94.0 KiB
First Owner
3721 
Second Owner
1691 
Third Owner
457 
Fourth & Above Owner
 
141
Test Drive Car
 
4

Length

Max length20
Median length11
Mean length11.49418
Min length11

Characters and Unicode

Total characters69126
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirst Owner
2nd rowSecond Owner
3rd rowFirst Owner
4th rowFirst Owner
5th rowFirst Owner

Common Values

ValueCountFrequency (%)
First Owner 3721
61.9%
Second Owner 1691
28.1%
Third Owner 457
 
7.6%
Fourth & Above Owner 141
 
2.3%
Test Drive Car 4
 
0.1%

Length

2024-11-27T18:38:55.775898image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T18:38:56.032843image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
owner 6010
48.8%
first 3721
30.2%
second 1691
 
13.7%
third 457
 
3.7%
fourth 141
 
1.1%
141
 
1.1%
above 141
 
1.1%
test 4
 
< 0.1%
drive 4
 
< 0.1%
car 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 10337
15.0%
e 7850
11.4%
n 7701
11.1%
6300
9.1%
O 6010
8.7%
w 6010
8.7%
i 4182
6.0%
t 3866
 
5.6%
F 3862
 
5.6%
s 3725
 
5.4%
Other values (14) 9283
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69126
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 10337
15.0%
e 7850
11.4%
n 7701
11.1%
6300
9.1%
O 6010
8.7%
w 6010
8.7%
i 4182
6.0%
t 3866
 
5.6%
F 3862
 
5.6%
s 3725
 
5.4%
Other values (14) 9283
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69126
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 10337
15.0%
e 7850
11.4%
n 7701
11.1%
6300
9.1%
O 6010
8.7%
w 6010
8.7%
i 4182
6.0%
t 3866
 
5.6%
F 3862
 
5.6%
s 3725
 
5.4%
Other values (14) 9283
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69126
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 10337
15.0%
e 7850
11.4%
n 7701
11.1%
6300
9.1%
O 6010
8.7%
w 6010
8.7%
i 4182
6.0%
t 3866
 
5.6%
F 3862
 
5.6%
s 3725
 
5.4%
Other values (14) 9283
13.4%

mileage
Real number (ℝ)

Distinct375
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.475944
Minimum0
Maximum42
Zeros14
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size94.0 KiB
2024-11-27T18:38:56.334753image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.9585
Q117
median19.44
Q322.32
95-th percentile25.83
Maximum42
Range42
Interquartile range (IQR)5.32

Descriptive statistics

Standard deviation3.984933
Coefficient of variation (CV)0.20460794
Kurtosis0.83579306
Mean19.475944
Median Absolute Deviation (MAD)2.64
Skewness-0.17914358
Sum117128.33
Variance15.879691
MonotonicityNot monotonic
2024-11-27T18:38:57.148280image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.44 195
 
3.2%
18.9 184
 
3.1%
19.7 146
 
2.4%
18.6 130
 
2.2%
21.1 124
 
2.1%
17 108
 
1.8%
15.96 98
 
1.6%
17.8 90
 
1.5%
16.1 86
 
1.4%
15.1 79
 
1.3%
Other values (365) 4774
79.4%
ValueCountFrequency (%)
0 14
0.2%
9 4
 
0.1%
9.5 1
 
< 0.1%
10 2
 
< 0.1%
10.1 2
 
< 0.1%
10.5 14
0.2%
10.71 1
 
< 0.1%
10.75 1
 
< 0.1%
10.8 1
 
< 0.1%
10.9 4
 
0.1%
ValueCountFrequency (%)
42 1
 
< 0.1%
33.44 2
 
< 0.1%
33 1
 
< 0.1%
32.52 1
 
< 0.1%
30.46 2
 
< 0.1%
28.4 74
1.2%
28.09 29
 
0.5%
27.62 5
 
0.1%
27.4 4
 
0.1%
27.39 21
 
0.3%

engine
Real number (ℝ)

High correlation 

Distinct120
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1425.7027
Minimum624
Maximum3604
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size94.0 KiB
2024-11-27T18:38:57.467268image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum624
5-th percentile796
Q11197
median1248
Q31498
95-th percentile2499
Maximum3604
Range2980
Interquartile range (IQR)301

Descriptive statistics

Standard deviation484.72854
Coefficient of variation (CV)0.33999272
Kurtosis1.1330892
Mean1425.7027
Median Absolute Deviation (MAD)213
Skewness1.2666541
Sum8574176
Variance234961.75
MonotonicityNot monotonic
2024-11-27T18:38:57.824389image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1248 985
16.4%
1197 604
 
10.0%
796 360
 
6.0%
998 343
 
5.7%
1498 292
 
4.9%
2179 290
 
4.8%
1396 234
 
3.9%
1199 167
 
2.8%
2523 160
 
2.7%
1461 148
 
2.5%
Other values (110) 2431
40.4%
ValueCountFrequency (%)
624 16
 
0.3%
793 5
 
0.1%
796 360
6.0%
799 61
 
1.0%
814 92
 
1.5%
909 2
 
< 0.1%
936 29
 
0.5%
993 24
 
0.4%
995 40
 
0.7%
998 343
5.7%
ValueCountFrequency (%)
3604 1
 
< 0.1%
3498 1
 
< 0.1%
3198 2
 
< 0.1%
2999 2
 
< 0.1%
2997 2
 
< 0.1%
2993 12
0.2%
2987 8
 
0.1%
2982 23
0.4%
2967 8
 
0.1%
2956 14
0.2%

max_power
Real number (ℝ)

High correlation 

Distinct313
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.681216
Minimum0
Maximum400
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size94.0 KiB
2024-11-27T18:38:58.157731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47.3
Q168
median81.83
Q399
95-th percentile147.835
Maximum400
Range400
Interquartile range (IQR)31

Descriptive statistics

Standard deviation31.554047
Coefficient of variation (CV)0.35987237
Kurtosis6.0134409
Mean87.681216
Median Absolute Deviation (MAD)14.78
Skewness1.7927489
Sum527314.84
Variance995.65791
MonotonicityNot monotonic
2024-11-27T18:38:58.516685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 282
 
4.7%
81.83 261
 
4.3%
88.5 168
 
2.8%
67 138
 
2.3%
46.3 136
 
2.3%
81.8 120
 
2.0%
62.1 119
 
2.0%
67.1 119
 
2.0%
67.04 117
 
1.9%
70 113
 
1.9%
Other values (303) 4441
73.8%
ValueCountFrequency (%)
0 3
 
< 0.1%
32.8 2
 
< 0.1%
34.2 18
 
0.3%
35 14
 
0.2%
35.5 2
 
< 0.1%
37 71
1.2%
37.48 8
 
0.1%
37.5 6
 
0.1%
38 1
 
< 0.1%
38.4 2
 
< 0.1%
ValueCountFrequency (%)
400 1
 
< 0.1%
282 1
 
< 0.1%
280 1
 
< 0.1%
272 1
 
< 0.1%
270.9 3
< 0.1%
265 1
 
< 0.1%
261.4 4
0.1%
258 2
< 0.1%
254.8 3
< 0.1%
254.79 1
 
< 0.1%

torque
Real number (ℝ)

High correlation 

Distinct390
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean478698.69
Minimum97171.602
Maximum2657704.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size94.0 KiB
2024-11-27T18:38:58.899368image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum97171.602
5-th percentile140567.27
Q1331348.24
median503377.91
Q3550022.78
95-th percentile855827.66
Maximum2657704.3
Range2560532.7
Interquartile range (IQR)218674.54

Descriptive statistics

Standard deviation238653.86
Coefficient of variation (CV)0.49854712
Kurtosis30.823539
Mean478698.69
Median Absolute Deviation (MAD)101655.91
Skewness3.7246463
Sum2.8788939 × 109
Variance5.6955664 × 1010
MonotonicityNot monotonic
2024-11-27T18:38:59.388019image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
401722 608
 
10.1%
700118.4509 346
 
5.8%
290556.2417 302
 
5.0%
390934.7246 138
 
2.3%
133590.2391 138
 
2.3%
570219.439 123
 
2.0%
252058.7395 119
 
2.0%
264351.3153 111
 
1.8%
901148.4752 101
 
1.7%
243902.1196 92
 
1.5%
Other values (380) 3936
65.4%
ValueCountFrequency (%)
97171.60227 88
1.5%
125352.2375 38
 
0.6%
133590.2391 138
2.3%
134997.6417 24
 
0.4%
140567.2662 40
 
0.7%
183005.6139 42
 
0.7%
204724.0976 43
 
0.7%
236821.3641 30
 
0.5%
238307.799 24
 
0.4%
243902.1196 92
1.5%
ValueCountFrequency (%)
2657704.311 28
 
0.5%
1233375.279 21
 
0.3%
1105190.99 20
 
0.3%
916763.4427 43
0.7%
911944.1646 23
 
0.4%
901148.4752 101
1.7%
889220.253 59
1.0%
855827.6623 42
0.7%
789830.1327 53
0.9%
758725.7954 44
0.7%

seats
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4238444
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size94.0 KiB
2024-11-27T18:38:59.808759image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q15
median5
Q35
95-th percentile7
Maximum14
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9789588
Coefficient of variation (CV)0.18049168
Kurtosis4.0881246
Mean5.4238444
Median Absolute Deviation (MAD)0
Skewness2.0143169
Sum32619
Variance0.95836034
MonotonicityNot monotonic
2024-11-27T18:39:00.311018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 4762
79.2%
7 820
 
13.6%
8 196
 
3.3%
4 97
 
1.6%
9 69
 
1.1%
6 49
 
0.8%
10 18
 
0.3%
2 2
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
2 2
 
< 0.1%
4 97
 
1.6%
5 4762
79.2%
6 49
 
0.8%
7 820
 
13.6%
8 196
 
3.3%
9 69
 
1.1%
10 18
 
0.3%
14 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
10 18
 
0.3%
9 69
 
1.1%
8 196
 
3.3%
7 820
 
13.6%
6 49
 
0.8%
5 4762
79.2%
4 97
 
1.6%
2 2
 
< 0.1%

Interactions

2024-11-27T18:38:38.885623image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:05.764335image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:10.077260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:14.283465image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:19.663924image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:24.750217image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:28.775505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:33.391596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:39.437343image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:06.233563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:10.499892image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:14.658766image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:20.454711image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:25.208526image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:29.216928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:34.428941image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:39.912846image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:06.658376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:10.961079image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:15.242499image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:20.916348image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:25.608395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:29.716104image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:35.089471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:40.321442image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:07.586745image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:11.486176image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:15.641696image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:21.773618image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:26.035220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:30.137386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:35.661408image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:40.903592image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:08.109063image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:12.063272image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:16.219609image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:22.110029image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:26.417032image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:30.622339image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:35.927550image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:41.355603image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:08.634825image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:12.510559image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:16.959111image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:22.956352image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:27.142095image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:31.235650image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:36.385063image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:41.943811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:09.024295image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:12.903736image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:17.873588image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:23.554047image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:27.714406image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:31.971428image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:37.078889image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:42.347066image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:09.564347image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:13.744389image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:18.734442image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:24.212967image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:28.320672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:32.761628image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T18:38:37.652924image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-27T18:39:00.726163image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
enginefuelkm_drivenmax_powermileageownerseatsseller_typeselling_pricetorquetransmissionyear
engine1.0000.4470.3050.715-0.4270.0760.5270.0990.4680.5630.400-0.037
fuel0.4471.0000.0360.1530.2900.0240.2140.0470.1010.2420.0350.134
km_driven0.3050.0361.0000.040-0.1970.0330.1940.000-0.294-0.0810.016-0.573
max_power0.7150.1530.0401.000-0.3080.0720.3060.1580.6170.6780.5130.170
mileage-0.4270.290-0.197-0.3081.0000.095-0.4360.0390.025-0.1330.2490.344
owner0.0760.0240.0330.0720.0951.0000.0210.1320.3630.0800.1180.259
seats0.5270.2140.1940.306-0.4360.0211.0000.0220.3200.3540.0330.050
seller_type0.0990.0470.0000.1580.0390.1320.0221.0000.1610.1030.2170.103
selling_price0.4680.101-0.2940.6170.0250.3630.3200.1611.0000.7190.4650.705
torque0.5630.242-0.0810.678-0.1330.0800.3540.1030.7191.0000.2180.399
transmission0.4000.0350.0160.5130.2490.1180.0330.2170.4650.2181.0000.153
year-0.0370.134-0.5730.1700.3440.2590.0500.1030.7050.3990.1531.000

Missing values

2024-11-27T18:38:43.081263image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-27T18:38:44.030415image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

nameyearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powertorqueseats
0Maruti Swift Dzire VDI2014450000145500DieselIndividualManualFirst Owner23.401248.074.00401722.0000005.0
1Skoda Rapid 1.5 TDI Ambition2014370000120000DieselIndividualManualSecond Owner21.141498.0103.52435612.9032915.0
2Hyundai i20 Sportz Diesel2010225000127000DieselIndividualManualFirst Owner23.001396.090.00332220.3721225.0
3Maruti Swift VXI BSIII2007130000120000PetrolIndividualManualFirst Owner16.101298.088.20521178.1624285.0
4Hyundai Xcent 1.2 VTVT E Plus201744000045000PetrolIndividualManualFirst Owner20.141197.081.86529258.5689255.0
5Maruti Wagon R LXI DUO BSIII200796000175000LPGIndividualManualFirst Owner17.301061.057.50521768.7775335.0
6Maruti 800 DX BSII2001450005000PetrolIndividualManualSecond Owner16.10796.037.0097171.6022734.0
7Toyota Etios VXD201135000090000DieselIndividualManualFirst Owner23.591364.067.10475105.9190195.0
8Ford Figo Diesel Celebration Edition2013200000169000DieselIndividualManualFirst Owner20.001399.068.10252058.7394965.0
9Renault Duster 110PS Diesel RxL201450000068000DieselIndividualManualSecond Owner19.011461.0108.45531436.2645335.0
nameyearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powertorqueseats
6986Maruti Alto LXi201120000073000PetrolIndividualManualFirst Owner19.70796.046.30133590.2391305.0
6987Maruti 800 AC199740000120000PetrolIndividualManualFirst Owner16.10796.037.0097171.6022734.0
6988Maruti Alto K10 VXI Airbag201734000045000PetrolIndividualManualFirst Owner23.95998.067.10290556.2417225.0
6990Hyundai i20 Magna201338000025000PetrolIndividualManualFirst Owner18.501197.082.85510634.9989545.0
6991Maruti Wagon R LXI Optional201736000080000PetrolIndividualManualFirst Owner20.51998.067.04290556.2417225.0
6992Hyundai Santro Xing GLS2008120000191000PetrolIndividualManualFirst Owner17.921086.062.10183005.6138645.0
6993Maruti Wagon R VXI BS IV with ABS201326000050000PetrolIndividualManualSecond Owner18.90998.067.10290556.2417225.0
6994Hyundai i20 Magna2013320000110000PetrolIndividualManualFirst Owner18.501197.082.85510634.9989545.0
6995Hyundai Verna CRDi SX2007135000119000DieselIndividualManualFourth & Above Owner16.801493.0110.00331105.6872075.0
6996Maruti Swift Dzire ZDi2009382000120000DieselIndividualManualFirst Owner19.301248.073.90401722.0000005.0